shibuiwilliam

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from keras.layers import Dense
from keras.models import Model


# create training data
def binary_encode(i, num_digits):

binaryfoundry

from keras.models import Sequential
from keras.layers import Dense
import numpy as np

np.random.seed(7)

NUM_DIGITS = 12

def binary_encode(i, num_digits):
    return np.array([i >> d & 1 for d in range(num_digits)])

autoML_classification.ipynb

joewiz

xquery version "3.1";

element environment-variables {
    for $var in available-environment-variables()
    order by $var
    return
        element environment-variable { 
            attribute name { $var },
            attribute value { environment-variable($var) }
        }

thomasjk10

import urllib
from BeautifulSoup import *

URL = raw_input("Enter the URL:") #Enter main URL
link_line = int(raw_input("Enter position:")) - 1 #The position of link relative to first link
count = int(raw_input("Enter count:")) #The number of times to be repeated

while count >= 0:
    html = urllib.urlopen(URL).read()
    soup = BeautifulSoup(html)

bgusach

def multireplace(string, replacements, ignore_case=False):
    """
    Given a string and a replacement map, it returns the replaced string.

    :param str string: string to execute replacements on
    :param dict replacements: replacement dictionary {value to find: value to replace}
    :param bool ignore_case: whether the match should be case insensitive
    :rtype: str

    """

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

PatrikHlobil

def iterate_all(iterable, returned="key"):
    
    """Returns an iterator that returns all keys or values
       of a (nested) iterable.
       
       Arguments:
           - iterable: <list> or <dictionary>
           - returned: <string> "key" or "value"
           
       Returns: